AI Health Literacy: a reflective framework for LLM-based generative AI in public health education and promotion

Area of Science:

• Public Health
• Health Communication
• Artificial Intelligence

Background:

• Generative AI, including large language models (LLMs), is transforming how individuals seek, comprehend, and utilize health information.
• AI tools can simplify, translate, and personalize health content, but may provide incomplete or outdated information.
• The potential for AI to shape public trust in health information presents a significant public health challenge.

Purpose of the Study:

• To conceptualize AI Health Literacy as an extension of existing literacy concepts, focusing on task and context sensitivity.
• To establish AI Health Literacy as an interdisciplinary judgment competence for evaluating AI-generated health information.
• To propose a framework for responsible AI health information use in public health education and promotion.

Main Methods:

• Conceptual analysis building upon health literacy, eHealth literacy, and digital health literacy.
• Development of a reflective framework with three dimensions: task appropriateness, context of use, and critical verifiability.
• Literature review on AI-mediated health communication and its implications for public health.

Main Results:

• AI Health Literacy requires assessing content, task, context, modality, verifiability, and psychological conditions of information reception.
• Generative AI can aid understanding and preparation but should not substitute professional medical advice in critical situations.
• A proposed framework supports education and guidance for responsible AI health information engagement.

Conclusions:

• AI Health Literacy is essential for navigating AI-generated health information effectively.
• A critical, reflective approach is needed to evaluate AI health outputs.
• Public health initiatives should promote AI Health Literacy for informed decision-making.